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| Author | Juan Andrade; T. Alejandra Vidal; A. Sanfeliu | ||||
| Title | Multirobot C-SLAM: Simultaneous localization, control, and mapping | Type | Miscellaneous | ||
| Year | 2005 | Publication | in Proc. IEEE ICRA05 Workshop on Network Robot Systems | Abbreviated Journal | |
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| Address | Barcelona (Spain) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ AVS2005b | Serial | 549 | ||
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| Author | Juan Andrade; T. Alejandra Vidal; A. Sanfeliu | ||||
| Title | Stochastic state estimation for simultaneous localization and map building in mobile robotics | Type | Book Chapter | ||
| Year | 2005 | Publication | Vedran Kordic, Aleksandar Lazinica, and Munir Merdan (Eds.), Cutting Edge Robotics, Advanced Robotic Systems Press, 3.3:223–242 | Abbreviated Journal | |
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| Notes | Approved | no | |||
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Admin @ si @ AVS2005a | Serial | 565 | ||
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| Author | Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Catastrophic interference in Disguised Face Recognition | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11868 | Issue | Pages | 64-75 | |
| Keywords | Neural network forgetness; Face recognition; Disguised Faces | ||||
| Abstract | It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number |
Admin @ si @ AVG2019 | Serial | 3416 | ||
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| Author | Jon Almazan; Ernest Valveny; Alicia Fornes | ||||
| Title | Deforming the Blurred Shape Model for Shape Description and Recognition | Type | Conference Article | ||
| Year | 2011 | Publication | 5th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 6669 | Issue | Pages | 1-8 | |
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| Abstract | This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance. | ||||
| Address | Las Palmas de Gran Canaria. Spain | ||||
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| Publisher | Springer-Verlag | Place of Publication | Berlin | Editor | Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG; | Approved | no | ||
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Admin @ si @ AVF2011 | Serial | 1732 | ||
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| Author | David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras | ||||
| Title | Visual Registration Method For A Low Cost Robot: Computer Vision Systems | Type | Conference Article | ||
| Year | 2009 | Publication | 7th International Conference on Computer Vision Systems | Abbreviated Journal | |
| Volume | 5815 | Issue | Pages | 204–214 | |
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| Abstract | An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. | ||||
| Address | Belgica | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-04666-7 | Medium | |
| Area | Expedition | Conference | ICVS | ||
| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ ATR2009b | Serial | 1247 | ||
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| Author | David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras | ||||
| Title | Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing | Type | Conference Article | ||
| Year | 2009 | Publication | 5th International Symposium on Visual Computing | Abbreviated Journal | |
| Volume | 5875 | Issue | Pages | 44–55 | |
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| Abstract | In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. | ||||
| Address | Las Vegas, USA | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-10330-8 | Medium | |
| Area | Expedition | Conference | ISVC | ||
| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ ATR2009a | Serial | 1246 | ||
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| Author | Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya | ||||
| Title | Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation | Type | Conference Article | ||
| Year | 2023 | Publication | European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval | Abbreviated Journal | |
| Volume | 13980 | Issue | Pages | 49–65 | |
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| Abstract | Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECIR | ||
| Notes | LAMP | Approved | no | ||
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Admin @ si @ ATK2023 | Serial | 3933 | ||
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| Author | Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe | ||||
| Title | Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks. |
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| Address | Washington;USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ ATE2017 | Serial | 3013 | ||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu | ||||
| Title | Low-dimensional and Comprehensive Color Texture Description | Type | Journal Article | ||
| Year | 2012 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 116 | Issue | I | Pages | 54-67 |
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| Abstract | Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1077-3142 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CAT;CIC | Approved | no | ||
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Admin @ si @ ASV2012 | Serial | 1827 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo | ||||
| Title | LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations | Type | Conference Article | ||
| Year | 2015 | Publication | 22th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 178 - 181 | ||
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| Address | Quebec; Canada; September 2015 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number |
Admin @ si @ AST2015 | Serial | 2630 | ||
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| Author | Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo | ||||
| Title | RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
| Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Multispectral Imaging; Free Sensor Model; Neural Network | ||||
| Abstract | This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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| Address | Porto; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | ADAS; MSIAU; 600.118; 600.122 | Approved | no | ||
| Call Number |
Admin @ si @ ASS2017 | Serial | 2918 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo | ||||
| Title | Automatic Static/Variable Content Separation in Administrative Document Images | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ ART2017 | Serial | 3001 | ||
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| Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados | ||||
| Title | A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 18 | Issue | 3 | Pages | 223-234 |
| Keywords | Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation | ||||
| Abstract | The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1433-2833 | ISBN | Medium | ||
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| Notes | DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 | Approved | no | ||
| Call Number |
Admin @ si @ ART2015 | Serial | 2679 | ||
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| Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados | ||||
| Title | Integrating Visual and Textual Cues for Query-by-String Word Spotting | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 511 - 515 | ||
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| Abstract | In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. | ||||
| Address | Washington; USA; August 2013 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; ADAS; 600.045; 600.055; 600.061 | Approved | no | ||
| Call Number |
Admin @ si @ ART2013 | Serial | 2224 | ||
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